Engineering Asset Management, that is the management and administration of large-scale industrial systems remains a challenging exercise. Industrial plants are extremely complex comprising many thousands, if not hundreds of thousands of parts that must work together to perform often automatized processes and operations. To assist in the design, maintenance and management of industrial parts, it is essential that plant operators maintain virtual models that accurately reflect the physical plant. These virtual models typically comprise hierarchically organised model elements. Not surprisingly these virtual models are hugely complex as they aim to describe and track both equipment and the individual components over the lifecycle of the physical plant such as tracing the fate of individual nuts and bolts over the lifecycle of an oil drilling rig. Due to the size and complexity these virtual models are often independently designed, constructed and maintained by different domains such as Engineering Procurement and Construction (EPC), and operation and maintenance. Frequently there is a need to exchange large quantities of data across domain boundaries of business-oriented and engineering-oriented systems. Further domain specific information systems may comprise many independently developed and maintained sub systems which are also required to exchange data.
For example, oil and gas processing companies are engaged in global networks for the engineering, procurement, construction and maintenance of their plants and assets (e.g., assembly of drilling platforms or refineries). These activities constantly require large-scale exchange of data between different sections of a company, between business partners in a venture, and between clients and providers in terms of equipment and services. One of the most important applications in the so-called Engineering Asset Management (EAM) domain is to exploit lifecycle data of industrial plants for more accurate planning and management of maintenance actions, thus saving substantial costs. Despite considerable efforts towards standardization of plant data management and exchange in the oil and gas industry, significant interoperability problems exist.
Different parts of the industry use competing standards for representing engineering assets—ISO 15926, and the Machinery Information Management Open Systems Alliance (MIMOSA) Open Systems Architecture for Enterprise Application Integration (OSA-EAI). Both standards facilitate the integration and exchange of heterogeneous information on industrial plants. The same holds for the lesser known ISO 14224 standard on maintenance systems descriptions. Engineering companies (i.e., equipment producers, design and CAD companies such as Intergraph or Bechtel) generally use ISO 15926. With certain exceptions, the maintenance providers and control systems companies (e.g., IBM/MAXIMO, Microsoft, Rockwell Automation, Emerson, and Yokogawa) use MIMOSA. Both sides have established a significant code base and market share-driven vested interest in maintaining a separate platform, even though any company would profit from superior interoperability.
The ISO 15926 standard defines an information model and exchange infrastructure for industrial plants. Although the title of the standard emphasizes the oil and gas industry, the data model is generic and allows representing assemble and lifecycle data of all kids of industrial plants. Formally, the data model of ISO 15926 is based on set theory and first order logic, specified in the EXPRESS modelling language. The model is also nominally defined in the OWL Semantic Web language, although its expressivity is insufficient to fully capture technical knowledge about the domain. As a result, OWL is mainly used for simple type consistency checks. The official ISO 15926 repository uses RDF (Resource Description Framework) as the internal representation.
MIMOSA OSA-EAI is rather specified in UML as an object-oriented information model which corresponds to the MIMOSA relational schema (CRIS). Basically OSA-EAI is an implementation of the ISO 13374 functional specification. This means that OSA-EAI complements the functionality blocks of the ISO 13374 standard with interface methods. The MIMOSA object and relational models are separately maintained but the object model is gaining in relative importance. Both ISO 15926 and MIMOSA nominally support XML as an interchange format. Many of the XML formats of ISO 15926 vendors are mutually non-interoperable, though, as the limited use of semantic annotations leaves too much flexibility in the implementation of the standard.
Thus integration of information within industrial systems remains a challenging task. As a result of the use of different data specifications or data standards two independently developed systems will typically find that their data structures are mutually inconsistent and thus non-interoperable. Whilst questions of low-level encoding and data types have become less of an issue with the rise of XML and its derivatives for data exchange, this has been offset by the higher complexity resulting from increased modelling power which has opened the interoperability gap even wider. As a result the transformation and exchange of data (ie data integration) is an ever-present issue. Whilst tools such as IBM Websphere or SAP Netweaver present an XML-syntax, service orientated architecture (SOA) based interface to the world to assist, the structure and meaning of data still needs to be manually adjusted by developers ensuring that the right data structures are exchanged and transformed at the right time. Whilst some tools exist for performing schema matching (eg MapForce) these can typically only accommodate very limited amounts of heterogeneity and thus in most EAM cases manual mappings are performed. However, this step of manually matching data structures from one data specification to another data specification is extremely time consuming and frequently quite specific to the specific systems or data exchange task (ie the transformation is not reusable). Some model driven engineering approaches have been developed to try and address these issues. One complex model based approach uses a large number of different model groups including semantic annotations, reasoning about Quality of Service requirements, ontology-based models in Resource Description Framework (RDF), Object Constraint Language (OCL) and process algebra, and an according family of supporting tools. However this approach is considerably complex and is still insufficient to completely handle real world conflict situations, and inevitably developers are required to fall back to manual mappings.
There is thus a need to provide computer implemented methods, tools and systems to facilitate mapping and exchange of model elements from information systems with different (ie heterogeneous) data specifications.